3. Our work with Learning Analytics
Tribal Learning Analytics R&D Project 3
4. “Every ….. days we create as much
information as we did from the dawn of
civilization up until 2003. That’s
something like five exabytes of data.”
Eric Schmidt (Google CEO)
5. Do we have Big Data in Higher
Education?
Tribal Learning Analytics R&D Project
6. Do we have Big Data in Higher
Education?
Yes, but…
Big is relative.
Tribal Learning Analytics R&D Project
7. Factors affecting Retention and
Success
Academic Integration Engagement Circumstances
Social Integration Preparation for HE
Tribal Learning Analytics R&D Project 7
8. Factors affecting Retention and
Success
Academic Integration Engagement Circumstances
Grades VLE Activity Social Background
Progress Library Activity Proximity
Student Debt
Social Integration Preparation for HE
Forum interaction Demographics
Social networks Qualifications
Tribal Learning Analytics R&D Project 8
9. Objectives for project
Supporting the student
Predict which students who may require additional support
Comparison to peers
Identify potential problem areas
Give staff better insight
Enable “actionable insights”
Steer students towards success
Tribal Learning Analytics R&D Project 9
12. Quantifying academic success
All students Cluster Individual
Student
Attainment of
Median grade
Median Grade cluster
for cluster
median grade
Tribal Learning Analytics R&D Project 12
13. Student Information System Activity Data Engagement
Academic Academic Integration
performance at Course Enrolment Attendance VLE Usage
entrance
Preparation for HE
Social Integration
Contact with
UCAS Application Fees Library Usage
support services
Future data sources
Social background Assessments Contact with tutors Campus PC Usage
Demographics Proximity Social interaction Door access
Open Data
IMD Spatial
Predictive Model
Tribal Learning Analytics R&D Project 13
14. Visualising Predictions
Predictions should help staff make informed decisions
Predictions from a model are just part of the picture
Predictions should be combined with staff experience and knowledge
Predictions should empower staff to ask the right questions
Predictions are a tool to help staff understand
where there might be issues and inform
subsequent discussions
Tribal Learning Analytics R&D Project 14
21. Summary
Student Success
Often focused on “academic success”
Are the current definitions of student success too simplistic?
Predictive Model
The model needs to be “transparent”
Allow practitioners to see where likely issues may lie
Combining diverse models results in greater predictive accuracy
Tribal Learning Analytics R&D Project 21
22. Summary
Data Visualisation for Learning Analytics
Should be focused on providing information to help inform discussions
Supplement predictions with analytics based on underlying activity data
Comparison with cohort enables comparative judgements to be made
Actionable Insights
Embedding intervention recording, management and workflow
Feedback loop to understand whether interventions make a difference
Tribal Learning Analytics R&D Project 22
5 exabytes – all words ever spoken by human beings throughout the whole of civilization.
Question is does HE have what we might call big data? And also can HE institutions extract knowledge from that data which can be used to help students?Yes, but…Big Data is relative – data sets generally smallerExamples… generally going to centre around electronic interactionsStudent Information System (e.g. SITS),VLE activity – Blackboard/Moodle (logs)Other logs of student activityOnline social interactionLots of opportunities and knowledge can be opened up by analysing multiple data sets which are available in FEWhat does this mean to education?Well, in the same way as user's of amazon, students leave a trail of data resulting from their interactions with university services.We can use this data to help us understand how well students are engaging academically and understand which patterns of engagement lead to improvements in the likely academic success.Learning lessons from how amazon uses data can enable an institution to become smarter about how they use the data they have.This is Learning Analytics - using the data we collect about students, to help us support the student, taking into account their particular needs, background and situation.
Question is does HE have what we might call big data? And also can HE institutions extract knowledge from that data which can be used to help students?Yes, but…Big Data is relative – data sets generally smallerExamples… generally going to centre around electronic interactionsStudent Information System (e.g. SITS),VLE activity – Blackboard/Moodle (logs)Other logs of student activityOnline social interactionLots of opportunities and knowledge can be opened up by analysing multiple data sets which are available in FEWhat does this mean to education?Well, in the same way as user's of amazon, students leave a trail of data resulting from their interactions with university services.We can use this data to help us understand how well students are engaging academically and understand which patterns of engagement lead to improvements in the likely academic success.Learning lessons from how amazon uses data can enable an institution to become smarter about how they use the data they have.This is Learning Analytics - using the data we collect about students, to help us support the student, taking into account their particular needs, background and situation.